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Dimensionality Reduction

706 directly classified papers

Papers per year

Papers

Efficient Dimensionality Reduction for High-Dimensional Network Estimation ICML 2014
Dimensionality Reduction with Subspace Structure Preservation NIPS 2014
Cone-Constrained Principal Component Analysis NIPS 2014
Nonnegative Sparse PCA with Provable Guarantees ICML 2014
Extracting Latent Structure From Multiple Interacting Neural Populations NIPS 2014
Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification CVPR 2014
Subspace Clustering for Sequential Data CVPR 2014
Provable Subspace Clustering: When LRR meets SSC NIPS 2013
Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning ACML 2013
Recovering the Optimal Solution by Dual Random Projection COLT 2013
Algorithms and Hardness for Robust Subspace Recovery COLT 2013
Learning a set of directions COLT 2013
A Scalable Unsupervised Feature Merging Approach to Efficient Dimensionality Reduction of High-Dimensional Visual Data ICCV 2013
Fast High Dimensional Vector Multiplication Face Recognition ICCV 2013
Robust Tucker Tensor Decomposition for Effective Image Representation ICCV 2013
A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map Super-resolution ICCV 2013
Randomized partition trees for exact nearest neighbor search COLT 2013
Complexity Theoretic Lower Bounds for Sparse Principal Component Detection COLT 2013
Analytic Bilinear Appearance Subspace Construction for Modeling Image Irradiance under Natural Illumination and Non-Lambertian Reflectance CVPR 2013
Low-Rank Matrix and Tensor Completion via Adaptive Sampling NIPS 2013
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform NIPS 2013
Bayesian inference for low rank spatiotemporal neural receptive fields NIPS 2013
Stochastic Optimization of PCA with Capped MSG NIPS 2013
Near-Optimal Entrywise Sampling for Data Matrices NIPS 2013
Memory Limited, Streaming PCA NIPS 2013